Nonparametric Estimation of Additive Model With Errors-in-Variables

Hao Dong () and Taisuke Otsu ()
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Taisuke Otsu: London School of Economics and Political Science

No 1812, Departmental Working Papers from Southern Methodist University, Department of Economics

Abstract: In estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement errors are resent in covariates. We propose an estimator for such models by extending Horowitz and Mammen’s (2004) two stage estimator for the errors-in-variables case. In the first stage, to adept to the additive structure, we use a series method together with a ridge approach to deal with ill-posedness brought by the mismeasurement. The uniform convergence rate for the first stage estimator is derived. To establish the limiting distribution, we consider the second stage estimator obtained by the one-step backfitting with a deconvolution kernel based on the first stage estimator.

Keywords: Additive model; nonparametric estimation; deconvolution. (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Nonparametric Estimation of Additive Model with Errors-in-Variables (2018)
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